To remedy this, we introduce a new Bayesian shape optimization algorithm, which we dub DEBOSH. We start with the GCNN of [1]} and give it the ability to estimate its own uncertainly using an ensemble-based approach similar to [2]}, [3]}, [4]}, [5]}. This enables us to rely on Bayesian Optimization (BO) to explore the shape space and re-train the network when necessary to produce shapes such as the one depicted by Fig. REF . As a result DEBOSH outperforms both earlier deep learning and classical approaches in terms of quality of the optimized shapes and required time to obtain them, which we demonstrate on several benchmarks.
